Typical adaptive neural control for hypersonic vehicle based on higher-order filters
Zhao Hewei, Rui Li
Abstract
A typical adaptive neural control methodology is used for the rigid body model of the hypersonic vehicle. The rigid body model is divided into the altitude subsystem and the velocity subsystem. The proportional integral differential (PID) controller is introduced to control the velocity track. The backstepping design is applied for constructing the controllers for the altitude subsystem. To avoid the explosion of differentiation from backstepping, the higher-order filter dynamic is used for replacing the virtual controller in the backstepping design steps. In the design procedure, the radial basis function (RBF) neural network is investigated to approximate the unknown nonlinear functions in the system dynamic of the hypersonic vehicle. The simulations show the effectiveness of the design method.